Triple
T14828775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | operations research |
E348641
|
entity |
| Predicate | typicalProblem |
P115995
|
FINISHED |
| Object | resource allocation |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: resource allocation | Statement: [operations research, typicalProblem, resource allocation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalProblem Context triple: [operations research, typicalProblem, resource allocation]
-
A.
problemType
Indicates the specific category or classification of a problem within a defined problem space or system.
-
B.
problemStatement
Indicates that an entity presents, defines, or expresses a specific problem or issue to be addressed.
-
C.
typicalQuestion
Indicates that an entity is a common or standard question typically asked in a given context or situation.
-
D.
problems
Indicates that one entity has issues, difficulties, or complications associated with or caused by another entity.
-
E.
commonlySolvedBy
Indicates that a problem, task, or issue is typically addressed or resolved through a particular method, tool, or agent.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d822eb8f588190bf53445e730a934f |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded0737d4c8190a49bf6b013da208c |
completed | April 14, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69de8c13418c819088ff9905ace1416a |
completed | April 14, 2026, 6:48 p.m. |
| PDg | Predicate description generation | batch_69de90806f3881908fcbfec5bd4ab4d2 |
completed | April 14, 2026, 7:07 p.m. |
Created at: April 10, 2026, 1:51 a.m.